
                        
                            import pandas as pd
import numpy as np
import plotly.graph_objects as go
from IPython.display import display, Markdown
import json
import requests
import time
from random import sample
from math import log2
from maayanlab_bioinformatics.dge import characteristic_direction, limma_voom
from maayanlab_bioinformatics.plotting.bridge import bridge_plot
import matplotlib.pyplot as plt
from os.path import exists


                        
                            import warnings
warnings.filterwarnings('ignore')


                        
                            # Set KO gene
ko_gene = 'NR1I2'

# Set working directory
l1000_data_dir = '../L1000_data'

# Set parameters 
low_expression_threshold = 0.3


                        
                            try: 
    expr_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv", sep='\t', index_col=0)
except: 
    l1000_data_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_data.tsv", sep='\t')

    l1000_data_list = []
    l1000_meta_list = []
    for row in l1000_data_df.itertuples(): 
        try:
            temp_df = pd.read_csv(row.persistent_id, sep='\t', index_col=0)
        except:
            print(f"Unable to access data from row {row.Index} at {row.persistent_id}")
            continue
        for col in temp_df.columns:
            l1000_meta_list.append([col] + l1000_data_df.loc[row.Index].tolist())
        l1000_data_list.append(temp_df)
    expr_df = pd.concat(l1000_data_list, axis=1)
expr_df.head()


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv"): 
    expr_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_fulldata.tsv' already exists!")

File at '../L1000_data/NR1I2_L1000_CRISPRKO_fulldata.tsv' already exists!


                        
                            try: 
    meta_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv", sep='\t', index_col=0)
except:
    meta_df = pd.DataFrame(l1000_meta_list, columns=['id'] + l1000_data_df.columns.tolist()).set_index('id')
if 'batch' not in meta_df.columns:
    meta_df['batch'] = meta_df.index.map(lambda x: '_'.join(x.split('_')[:3]))
meta_df.head()


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv"): 
    meta_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_CRISPRKO_metadata.tsv' already exists!")

File at '../L1000_data/NR1I2_L1000_CRISPRKO_metadata.tsv' already exists!


                        
                            batches = meta_df['batch'].unique()
len(batches)

10


                        
                            ctrl_data_df = pd.read_csv(f"{l1000_data_dir}/L1000_Controls.tsv", sep='\t')
ctrl_data_df = ctrl_data_df[ctrl_data_df['batch'].isin(batches)]
set(batches).difference(ctrl_data_df['batch'])

set()


                        
                            ctrl_data_df.head()


                        
                            try: 
    ctrl_expr_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv", sep='\t', index_col=0)
    ctrl_meta_df = pd.read_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv", sep='\t', index_col=0)
except:
    ctrl_data_list = []
    ctrl_meta_list = []
    for row in ctrl_data_df.itertuples():
        try: 
            temp_df = pd.read_csv(row.persistent_id, sep='\t', index_col=0)
        except:
            print(f"Unable to access data from row {row.Index} at {row.persistent_id}")
            continue
        for col in temp_df.columns: 
            ctrl_meta_list.append([col, row.batch])
        ctrl_data_list.append(temp_df)

    ctrl_expr_df = pd.concat(ctrl_data_list, axis=1)
    ctrl_meta_df = pd.DataFrame(ctrl_meta_list, columns=['id', 'batch']).set_index('id')


                        
                            ctrl_expr_df.head()


                        
                            ctrl_meta_df.head()


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv"): 
    ctrl_expr_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_Controls_fulldata.tsv' already exists!")


                        
                            if not exists(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv"): 
    ctrl_meta_df.to_csv(f"{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv", sep='\t', index=True)
else: 
    print(f"File at '{l1000_data_dir}/{ko_gene}_L1000_Controls_metadata.tsv' already exists!")


                        
                            combined_df = pd.concat([
    expr_df.groupby(expr_df.index).mean(), 
    ctrl_expr_df.groupby(ctrl_expr_df.index).mean()
], axis=1)
combined_df.head()


                        
                            datasets = {
    'metadata': meta_df, 
    'ctrl_metadata': ctrl_meta_df,
    'rawdata': combined_df
}


                        
                            batch_profiles = {x: {'perts': [], 'ctrls': []} for x in batches}
for b in batches: 
    batch_profiles[b]['perts'] = meta_df[meta_df['batch'] == b].index.tolist()
    batch_profiles[b]['ctrls'] = ctrl_meta_df[ctrl_meta_df['batch'] == b].index.tolist()


                        
                            batch_signatures = {'cd': {}, 'limma': {}, 'fc': {}}


                        
                            # Function for computing signatures with characteristic direction
def cd_signature(ctrl_ids, case_ids, dataset, normalization):
  
    signature = characteristic_direction(
        dataset[normalization].loc[:, ctrl_ids], 
        dataset[normalization].loc[:, case_ids], 
        calculate_sig=True
    )
    signature = signature.sort_values("CD-coefficient", ascending=False)
    
    return signature


                        
                            for b in batches: 
    batch_signatures['cd'][b] = cd_signature(
        batch_profiles[b]['ctrls'], 
        batch_profiles[b]['perts'],
        datasets, 
        'rawdata'
    )


                        
                            # Function for computing signatures
def limma(ctrl_ids, case_ids, dataset, normalization):
    
    signature = limma_voom.limma_voom_differential_expression(
        dataset[normalization].loc[:, ctrl_ids],
        dataset[normalization].loc[:, case_ids],
        filter_genes=False
    )
    signature = signature.sort_values("t", ascending=False)

    return signature


                        
                            for b in batches: 
    batch_signatures['limma'][b] = limma(
        batch_profiles[b]['ctrls'], 
        batch_profiles[b]['perts'], 
        datasets, 
        'rawdata'
    )

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                            # Function for computing signatures with fold change
def logFC(ctrl_ids, case_ids, dataset, normalization):

    case_mean = dataset[normalization].loc[:, case_ids].mean(axis=1)
    ctrl_mean = dataset[normalization].loc[:, ctrl_ids].mean(axis=1)
    
    signature = case_mean / ctrl_mean

    signature = signature.apply(lambda x: log2(x+1)).rename('logFC')
    
    return pd.DataFrame(signature.sort_values(ascending=False))


                        
                            for b in batches: 
    batch_signatures['fc'][b] = logFC(
        batch_profiles[b]['ctrls'], 
        batch_profiles[b]['perts'],
        datasets,
        'rawdata'
    )


                        
                            batch_signatures['cd_all']= pd.concat([
    df[['CD-coefficient']].rename(columns={'CD-coefficient': b}) for (b, df) in batch_signatures['cd'].items()
], axis=1).sort_index()
batch_signatures['limma_all'] = pd.concat([
    df[['t']].rename(columns={'t': b}) for (b, df) in batch_signatures['limma'].items()
], axis=1).sort_index()
batch_signatures['fc_all'] = pd.concat([
    df.rename(b) for b, df in batch_signatures['fc'].items()
], axis=1).sort_index()

for k in batch_signatures.keys(): 
    if k.endswith('_all'): 
        method = k.split('_')[0].upper()
        display(Markdown(f"All {method} batch signatures"))
        display(batch_signatures[k])


                        
                            # Function to get Enrichr Results
def Enrichr_API(enrichr_gene_list, library_name, signame, method, direction):

    all_ranks = []
    all_terms = []
    all_pvalues =[] 
    all_adjusted_pvalues = []

    ENRICHR_URL = 'https://maayanlab.cloud/Enrichr/addList'
    genes_str = '\n'.join(enrichr_gene_list)
    description = f'Dex benchmark {signame} {method} {direction} genes'
    payload = {
        'list': (None, genes_str),
        'description': (None, description)
    }

    response = requests.post(ENRICHR_URL, files=payload)
    if not response.ok:
        raise Exception('Error uploading gene list')

    list_data = json.loads(response.text)
    time.sleep(0.5)
    ENRICHR_URL = 'https://maayanlab.cloud/Enrichr/enrich'
    response = requests.get(
        ENRICHR_URL + f"?userListId={list_data['userListId']}&backgroundType={library_name}"
    )
    if not response.ok:
        raise Exception('Error fetching enrichment results')

    enrich_data = json.loads(response.text)
    results_df  = pd.DataFrame(enrich_data[library_name])
    all_ranks.append(list(results_df[0]))
    all_terms.append(list(results_df[1]))
    all_pvalues.append(list(results_df[2]))
    all_adjusted_pvalues.append(list(results_df[6]))

    res_dict = {
        'ranks': all_ranks, 
        'terms': all_terms, 
        'pvals': all_pvalues,
        'adjpvals': all_adjusted_pvalues, 
        'shortid': str(list_data['shortId'])
    }

    return res_dict


                        
                            # Get gene lists to put into Enrichr
gene_lists = {}
for m in batch_signatures.keys():
    if 'all' in m:
        mname = m.split('_')[0]
        gene_lists[mname] = {'up': {}, 'down': {}, 'combined': {}}
        for sig in batch_signatures[m]: 
            gene_lists[mname]['up'][sig] = batch_signatures[m][sig].sort_values(ascending=False).index.tolist()[:250]
            gene_lists[mname]['down'][sig] = batch_signatures[m][sig].sort_values(ascending=True).index.tolist()[:250]
            gene_lists[mname]['combined'][sig] = gene_lists[mname]['up'][sig] + gene_lists[mname]['down'][sig]


                        
                            # Get results
results = {}
for m in gene_lists.keys():
    results[m] = {'up': {}, 'down': {}, 'combined': {}}
    for d in gene_lists[m].keys():
        for sig in gene_lists[m][d].keys():
            results[m][d][sig]= Enrichr_API(
                gene_lists[m][d][sig], 'ChEA_2022', sig, m, d
            )


                        
                            # Extract dexamethasone target rankings
# Initialize lists for storing target information
ranks = []


                        
                            display(Markdown(f"## {ko_gene} Term Rankings"))


                        
                            for m in results.keys(): 
    for d in results[m].keys():
        for sig in results[m][d].keys():
            for i in range(len(results[m][d][sig]['ranks'][0])): 
                if ko_gene in results[m][d][sig]['terms'][0][i]: 
                    ranks.append([
                        f"{sig}:{m}:{d}",
                        results[m][d][sig]['terms'][0][i], 
                        results[m][d][sig]['ranks'][0][i],
                        results[m][d][sig]['pvals'][0][i]
                    ])
full_df = pd.DataFrame(ranks, columns=['Gene_Set', 'Term', 'Rank', 'p-value'])
full_df['Method'] = full_df['Gene_Set'].apply(lambda x: x.split(':')[1])
full_df['Direction'] = full_df['Gene_Set'].apply(lambda x : x.split(':')[2])
full_df['Method_Direction'] = full_df.apply(lambda row: row['Method'] + ':' + row['Direction'], axis=1)


                        
                            up_df = full_df[full_df['Direction']=='up']
down_df = full_df[full_df['Direction']=='down']
combined_df = full_df[full_df['Direction']=='combined']


                        
                            display(Markdown(f"Mean rank of {ko_gene} terms from ChEA 2022 for up genes from each method."))
display(up_df.groupby(['Method']).mean(numeric_only=True).sort_values(['Rank', 'Method']).drop(['p-value'], axis=1))
display(Markdown(f"Mean rank of {ko_gene} terms from ChEA 2022 for down genes from each method."))
display(down_df.groupby(['Method']).mean(numeric_only=True).sort_values(['Rank', 'Method']).drop(['p-value'], axis=1))
display(Markdown(f"Mean rank of {ko_gene} terms from ChEA 2022 for combined up and down genes from each method."))
display(combined_df.groupby(['Method']).mean(numeric_only=True).sort_values(['Rank', 'Method']).drop(['p-value'], axis=1))


                        
                            color_dict = {
    'up': 'crimson',
    'down': '#636EFA', 
    'combined': 'gray'
}

fig1 = go.Figure()
for gs in full_df.groupby('Method_Direction').mean().sort_values('Rank').index:
    fig1.add_trace(
        go.Box(
            y=full_df[full_df['Method_Direction']==gs]['Rank'].tolist(),
            name=gs.split(':')[0].upper() + ':' + gs.split(':')[1],
            marker_color=color_dict[gs.split(':')[1]]
        )
    )
fig1.update_layout(
    title_text=f"{ko_gene} Term Rankings for L1000 Gene Sets by Method and Direction",
    xaxis={
        'title': {'text': 'Method:Direction'}, 
    },
    yaxis={
        'title': {'text': 'Rank'}
    },
    showlegend=False
)
fig1.show("png")


                        
                            color_dict = {
    'cd': 'green',
    'limma': '#b76935', 
    'fc': 'purple'
}

fig1 = go.Figure()
for gs in full_df.groupby('Method_Direction').mean().sort_values('Rank').index:
    fig1.add_trace(
        go.Box(
            y=full_df[full_df['Method_Direction']==gs]['Rank'].tolist(),
            name=gs.split(':')[0].upper() + ':' + gs.split(':')[1],
            marker_color=color_dict[gs.split(':')[0]]
        )
    )
fig1.update_layout(
    title_text=f"{ko_gene} Term Rankings for L1000 Gene Sets by Method and Direction",
    xaxis={
        'title': {'text': 'Method:Direction'}, 
    },
    yaxis={
        'title': {'text': 'Rank'}
    },
    showlegend=False
)
fig1.show("png")


                        
                            full_df['Batch'] = full_df['Gene_Set'].apply(lambda x: x.split(':')[0])
fig1 = go.Figure()
for gs in full_df['Batch'].unique():
    fig1.add_trace(
        go.Box(
            y=full_df[full_df['Batch']==gs]['Rank'].tolist(),
            name=gs,
            marker_color='#636EFA'
        )
    )
fig1.update_layout(
    title_text=f"{ko_gene} Term Rankings for L1000 Gene Sets by Batch",
    xaxis={
        'title': {'text': 'Batch'}, 
    },
    yaxis={
        'title': {'text': 'Rank'}
    },
    showlegend=False
)
fig1.show("png")


                        
                            def getEnrichrGeneSets(libname, termlist): 
    term_dict = {}
    liburl = f'https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=json&libraryName={libname}'
    response = requests.get(liburl)
    if not response.ok:
        raise Exception('Error fetching library from Enrichr')
    for term in termlist:
        geneset = response.json()[libname]['terms'][term].keys()
        term_dict[term] = geneset
    return term_dict

target_genesets = getEnrichrGeneSets('ChEA_2022', full_df['Term'].unique())


                        
                            def methodBridgePlot(gsname, method, colname, abs_val=False): 
    geneset = target_genesets[gsname]
    y = []

    for k in batch_signatures[method].keys():
        if abs_val: 
            sig = batch_signatures[method][k][colname].apply(abs)
        else: 
            sig = batch_signatures[method][k][colname]
        select = pd.Series(
            [x.upper() in geneset for x in sig.sort_values(ascending=False).index.tolist()]
        )   
        x, y_temp = bridge_plot(select)
        y.append(y_temp)
    
    x = x/len(x)
    return x, np.mean(y, axis=0)

def randomBridgePlot(gsname): 
    all_x = []
    all_y = []

    for _ in range(10):
        rand_sig = sample(
            batch_signatures['cd_all'].index.tolist(), 
            batch_signatures['cd_all'].shape[0]
        )
        select = pd.Series([
            x.upper() in target_genesets[gsname] for x in rand_sig
        ])
        x, y = bridge_plot(select)
        x = x/len(x)
        all_x.append(x)
        all_y.append(y)

    return all_x, all_y


                        
                            colnames = {
    'cd': 'CD-coefficient', 
    'limma': 't', 
    'fc': 'logFC'
}


                        
                            def build_res(abs_val=False):
    res = {}
    for term in target_genesets.keys():
        res[term] = {'random': {'x': [], 'y': []}}
        rand_x, rand_y = randomBridgePlot(term)
        res[term]['random']['x'] += rand_x
        res[term]['random']['y'] += rand_y
        for method in batch_signatures.keys():
            if 'all' not in method:
                temp_x, temp_y = methodBridgePlot(term, method, colnames[method], abs_val=abs_val)
                if method in res[term].keys():
                    res[term][method]['x'].append(temp_x)
                    res[term][method]['y'].append(temp_y)
                else: 
                    res[term][method] = {'x': [temp_x], 'y': [temp_y]}
    return res

method_results = build_res(abs_val=False)
method_results_abs = build_res(abs_val=True)


                        
                            def drawBridgePlot(abs_val=False):
    if abs_val:
        res = method_results_abs
    else: 
        res = method_results
    for term in res.keys():
        for sig in res[term].keys(): 
            term_x = np.mean(res[term][sig]['x'], axis=0)
            term_y = np.mean(res[term][sig]['y'], axis=0)
            if sig == 'random': 
                plt.plot(term_x, term_y, label=sig, color='gray')
            elif sig == 'CD': 
                plt.plot(term_x-(0.01*max(term_x)), term_y, label=sig)
            else:
                plt.plot(term_x, term_y, label=sig)

        plt.axhline(y=0, color='black', linestyle='dashed')
        plt.legend(bbox_to_anchor=(1, 1))
        if abs_val:
            plt.title(f"{term.split(' ')[0]} Target Gene Rankings for Each Method (Abs Val)")
        else:
            plt.title(f"{term.split(' ')[0]} Target Gene Rankings for Each Method")
        plt.show()


                        
                            drawBridgePlot()

	XPR010_A375.311_96H_X1_B35:F08	XPR010_A375.311_96H_X2_B35:F08	XPR010_A375.311_96H_X3_B35:F08	XPR010_A375.311_96H_X1_B35:F24	XPR010_A375.311_96H_X2_B35:F24	XPR010_A375.311_96H_X3_B35:F24	XPR010_A549.311_96H_X1.L2_B36:F08	XPR010_A549.311_96H_X3_B35:F08	XPR010_A549.311_96H_X1.L2_B36:F24	XPR010_A549.311_96H_X3_B35:F24	...	XPR010_U251MG.311_96H_X3_B35:F08	XPR010_U251MG.311_96H_X1_B35:F24	XPR010_U251MG.311_96H_X2_B35:F24	XPR010_U251MG.311_96H_X3_B35:F24	XPR010_YAPC.311_96H_X1_B35:F08	XPR010_YAPC.311_96H_X2_B35:F08	XPR010_YAPC.311_96H_X3_B35:F08	XPR010_YAPC.311_96H_X1_B35:F24	XPR010_YAPC.311_96H_X2_B35:F24	XPR010_YAPC.311_96H_X3_B35:F24
symbol
DDR1	6.158450	6.202200	6.285650	6.249500	6.071100	6.225575	6.03340	6.26310	6.29285	6.52600	...	6.41030	6.8449	6.698900	6.50580	6.553050	7.549575	6.84630	6.780425	6.951875	6.821550
PAX8	6.011775	6.106200	5.664900	5.665750	5.471950	5.780600	4.99225	5.30080	4.62410	4.82470	...	5.23020	4.5078	5.127000	5.00560	5.445525	5.153600	4.25930	5.035250	4.937550	4.004100
GUCA1A	4.835400	4.938175	4.842400	4.697725	4.872650	4.826450	5.21620	5.17145	4.99370	5.02665	...	4.89295	4.7672	4.806500	4.92165	5.290500	5.622600	5.89155	5.574350	5.208800	5.685100
EPHB3	6.632500	6.971400	6.574300	6.753650	6.427550	6.769800	8.29650	7.51050	7.67320	8.04430	...	8.03480	8.9509	7.990300	7.93880	7.675250	7.396850	6.47865	7.833150	7.967200	7.705101
ESRRA	7.819700	7.858900	8.497725	8.272900	8.056351	7.891300	8.73630	8.28170	8.84240	8.22250	...	7.25300	6.8591	7.123001	7.13630	8.613050	8.440750	7.94130	8.899151	8.561750	8.045325

	tissue	disease	cell_line	pert_name	pert_time	pert_type	data_level	creation_time	persistent_id	pert_dose	batch
id
XPR010_A375.311_96H_X1_B35:F08	skin of body	melanoma	A375.311	NR1I2	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR010_A375.311_96H
XPR010_A375.311_96H_X2_B35:F08	skin of body	melanoma	A375.311	NR1I2	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR010_A375.311_96H
XPR010_A375.311_96H_X3_B35:F08	skin of body	melanoma	A375.311	NR1I2	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR010_A375.311_96H
XPR010_A375.311_96H_X1_B35:F24	skin of body	melanoma	A375.311	NR1I2	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR010_A375.311_96H
XPR010_A375.311_96H_X2_B35:F24	skin of body	melanoma	A375.311	NR1I2	96 h	CRISPR Knockout	3	2021-01-21	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	NaN	XPR010_A375.311_96H

	local_id	persistent_id	batch
8910	L1000_LINCS_DCIC_2021_XPR010_A375.311_96H_A06_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR010_A375.311_96H
8911	L1000_LINCS_DCIC_2021_XPR010_A375.311_96H_D05_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR010_A375.311_96H
8912	L1000_LINCS_DCIC_2021_XPR010_A375.311_96H_D20_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR010_A375.311_96H
8913	L1000_LINCS_DCIC_2021_XPR010_A375.311_96H_D21_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR010_A375.311_96H
8914	L1000_LINCS_DCIC_2021_XPR010_A375.311_96H_E13_...	https://lincs-dcic.s3.amazonaws.com/LINCS-data...	XPR010_A375.311_96H

	XPR010_A375.311_96H_X1_B35:A06	XPR010_A375.311_96H_X2_B35:A06	XPR010_A375.311_96H_X3_B35:A06	XPR010_A375.311_96H_X1_B35:D05	XPR010_A375.311_96H_X2_B35:D05	XPR010_A375.311_96H_X3_B35:D05	XPR010_A375.311_96H_X1_B35:D20	XPR010_A375.311_96H_X2_B35:D20	XPR010_A375.311_96H_X3_B35:D20	XPR010_A375.311_96H_X1_B35:D21	...	XPR010_YAPC.311_96H_X3_B35:E14	XPR010_YAPC.311_96H_X1_B35:F04	XPR010_YAPC.311_96H_X2_B35:F04	XPR010_YAPC.311_96H_X3_B35:F04	XPR010_YAPC.311_96H_X1_B35:H24	XPR010_YAPC.311_96H_X2_B35:H24	XPR010_YAPC.311_96H_X3_B35:H24	XPR010_YAPC.311_96H_X1_B35:K17	XPR010_YAPC.311_96H_X2_B35:K17	XPR010_YAPC.311_96H_X3_B35:K17
symbol
NAT2	7.260575	7.280875	7.02010	6.966801	7.45305	7.79865	7.34975	7.165100	7.708175	7.48150	...	12.6010	8.773951	8.02120	12.157150	7.691299	7.311550	12.085751	8.362350	9.20055	11.97370
ADA	5.064700	5.259250	5.22435	5.144300	5.38295	5.03075	5.07075	5.199075	4.932025	5.28315	...	4.6644	4.806450	4.66950	3.391350	5.972750	5.615875	4.137550	6.133075	4.75340	4.23540
CDH2	5.706775	5.822050	5.69430	5.853000	5.64155	5.71160	5.98715	5.800150	5.671200	5.80255	...	6.0781	5.741250	5.86595	5.915725	5.294300	6.179800	6.161100	5.736300	5.83550	5.75765
AKT3	0.896850	2.521300	2.37685	1.295950	3.11635	2.51195	2.38330	1.790250	1.295950	3.40630	...	0.0000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000
MED6	5.884050	5.757250	5.77825	5.589350	6.05750	5.44470	5.63980	5.929700	5.832875	5.86350	...	6.2660	5.605700	6.17515	5.849500	5.851400	6.168350	5.662300	5.871375	6.37535	6.18240

	batch
id
XPR010_A375.311_96H_X1_B35:A06	XPR010_A375.311_96H
XPR010_A375.311_96H_X2_B35:A06	XPR010_A375.311_96H
XPR010_A375.311_96H_X3_B35:A06	XPR010_A375.311_96H
XPR010_A375.311_96H_X1_B35:D05	XPR010_A375.311_96H
XPR010_A375.311_96H_X2_B35:D05	XPR010_A375.311_96H

Benchmarking the Recovery of Known Targets from L1000 CRISPR KO Data: Batch Version¶

KO Gene: NR1I2

Load in Data¶

Load in Control Data¶

Normalize Data¶

Combine data and remove duplicate genes¶

Compute Signatures: Batch Perturbations vs. Batch Controls¶

Characteristic Direction¶

Limma¶

Fold Change¶

All signatures¶

Enrichment Analysis Rankings¶

NR1I2 Term Rankings¶

Bridge plots¶

	XPR010_A375.311_96H_X1_B35:F08	XPR010_A375.311_96H_X2_B35:F08	XPR010_A375.311_96H_X3_B35:F08	XPR010_A375.311_96H_X1_B35:F24	XPR010_A375.311_96H_X2_B35:F24	XPR010_A375.311_96H_X3_B35:F24	XPR010_A549.311_96H_X1.L2_B36:F08	XPR010_A549.311_96H_X3_B35:F08	XPR010_A549.311_96H_X1.L2_B36:F24	XPR010_A549.311_96H_X3_B35:F24	...	XPR010_YAPC.311_96H_X3_B35:E14	XPR010_YAPC.311_96H_X1_B35:F04	XPR010_YAPC.311_96H_X2_B35:F04	XPR010_YAPC.311_96H_X3_B35:F04	XPR010_YAPC.311_96H_X1_B35:H24	XPR010_YAPC.311_96H_X2_B35:H24	XPR010_YAPC.311_96H_X3_B35:H24	XPR010_YAPC.311_96H_X1_B35:K17	XPR010_YAPC.311_96H_X2_B35:K17	XPR010_YAPC.311_96H_X3_B35:K17
symbol
A1CF	4.45315	4.23635	4.634250	3.93165	3.87275	3.75120	5.3409	4.67510	5.60880	5.23860	...	10.192900	10.741025	10.62880	10.17440	11.360250	10.609800	10.58320	11.2271	11.164000	10.640949
A2M	7.22560	7.47065	8.596849	7.24335	7.29490	7.76080	8.1608	5.55060	8.57160	5.88430	...	8.146475	7.649900	8.25165	8.60140	7.470325	7.525375	7.99345	7.0502	8.668000	8.725750
A4GALT	5.43295	5.44650	5.665200	5.29545	5.68145	5.51620	6.7321	6.05495	6.72805	5.74780	...	5.415850	6.452750	5.52640	5.43765	5.709750	6.054850	4.93555	6.1569	6.114300	4.902300
A4GNT	5.37260	5.28950	5.293250	5.18680	5.30390	5.38095	5.2655	5.52010	5.44740	5.62385	...	8.344800	8.872550	8.37400	8.79715	8.576900	8.662701	8.30970	8.8629	8.714951	8.285049
AAAS	7.51980	7.64780	7.956600	7.59620	8.07660	7.87480	8.1372	7.55920	8.28530	7.90890	...	6.719550	6.666550	6.71455	6.97565	6.509600	6.576250	6.78680	6.4280	6.527250	6.776250

	XPR010_A375.311_96H	XPR010_A549.311_96H	XPR010_AGS.311_96H	XPR010_BICR6.311_96H	XPR010_ES2.311_96H	XPR010_HT29.311_96H	XPR010_MCF7.311_96H	XPR010_PC3.311B_96H	XPR010_U251MG.311_96H	XPR010_YAPC.311_96H
symbol
A1CF	-0.019577	-0.016974	-0.015616	-0.016290	-0.020444	-0.018519	-0.017728	-0.020971	-0.017763	-0.019295
A2M	0.003215	-0.003538	0.001771	-0.001684	-0.001222	-0.002409	0.001087	0.002743	0.001958	0.004480
A4GALT	-0.000740	0.000667	0.000689	0.002151	0.000054	-0.001610	-0.000614	0.000041	-0.000140	0.000447
A4GNT	-0.011040	-0.008234	-0.014184	-0.013903	-0.014138	-0.015587	-0.013585	-0.012055	-0.013713	-0.009598
AAAS	0.004927	0.005585	0.005249	0.003679	0.005078	0.004642	0.005265	0.004076	0.004921	0.003304
...	...	...	...	...	...	...	...	...	...	...
ZXDB	0.000307	-0.001142	-0.000563	-0.000166	-0.001886	-0.001636	-0.000132	-0.000568	-0.001656	0.000642
ZXDC	-0.004677	-0.009372	-0.007265	-0.002839	-0.005829	-0.001725	-0.007841	-0.008623	-0.010583	-0.009344
ZYX	0.018972	0.014685	0.018484	0.012741	0.019393	0.011563	0.008750	0.022170	0.016844	0.015572
ZZEF1	-0.007563	-0.007977	-0.001991	-0.002495	-0.005057	-0.001901	0.006034	-0.004659	0.000046	-0.003763
ZZZ3	0.005001	0.008117	0.002834	0.004480	0.009239	0.003054	-0.000552	0.003891	0.002508	0.001957

	XPR010_A375.311_96H	XPR010_A549.311_96H	XPR010_AGS.311_96H	XPR010_BICR6.311_96H	XPR010_ES2.311_96H	XPR010_HT29.311_96H	XPR010_MCF7.311_96H	XPR010_PC3.311B_96H	XPR010_U251MG.311_96H	XPR010_YAPC.311_96H
A1CF	-60.019539	-28.815911	-44.964117	-42.496500	-76.125622	-40.349357	-38.287313	-65.604340	-55.876038	-43.141008
A2M	4.845533	-1.847583	3.406957	-2.852660	-2.416547	-0.537058	1.565255	9.006032	3.824993	2.729793
A4GALT	-3.617646	-1.016386	0.559526	6.139557	-2.310624	-4.533725	-3.529359	0.950890	-2.583732	2.579730
A4GNT	-42.919025	-19.718965	-50.182363	-30.878052	-47.377980	-36.774901	-24.278347	-24.678781	-38.743252	-27.158267
AAAS	26.282910	13.767572	18.236763	14.897501	24.929960	13.017880	16.846031	16.416623	23.690760	14.218616
...	...	...	...	...	...	...	...	...	...	...
ZXDB	0.235291	-2.809396	-0.987368	1.787694	-6.089417	-4.552719	-0.102972	-0.915882	-4.805944	-6.810436
ZXDC	-16.760846	-11.868575	-15.712726	-6.614901	-12.108318	-1.424355	-16.841576	-23.859809	-22.600620	-8.387029
ZYX	36.112290	13.855560	23.705964	20.629454	37.057409	15.960206	11.385518	35.864251	30.875875	25.291581
ZZEF1	-19.567202	-7.184474	-1.622090	-7.559272	-15.772091	-9.197690	3.419958	-8.114486	-3.014485	-9.025222
ZZZ3	17.788706	4.820815	6.428117	8.105470	28.521079	8.612288	3.189601	9.125449	9.904261	2.446274

	XPR010_A375.311_96H	XPR010_A549.311_96H	XPR010_AGS.311_96H	XPR010_BICR6.311_96H	XPR010_ES2.311_96H	XPR010_HT29.311_96H	XPR010_MCF7.311_96H	XPR010_PC3.311B_96H	XPR010_U251MG.311_96H	XPR010_YAPC.311_96H
symbol
A1CF	-1.418332	-1.040272	-1.136226	-1.194198	-1.601675	-1.222738	-1.217520	-1.387034	-1.239450	-1.206063
A2M	0.157266	-0.123163	0.240870	-0.164634	-0.072182	-0.026677	0.108978	0.272596	0.159253	0.225869
A4GALT	-0.076240	-0.044683	0.009301	0.139864	-0.056234	-0.118184	-0.108340	0.020024	-0.071076	0.106850
A4GNT	-0.767693	-0.735056	-0.931857	-0.862885	-0.898295	-0.979380	-0.801131	-0.709310	-0.933009	-0.580846
AAAS	0.345681	0.417361	0.329596	0.214427	0.396350	0.324106	0.314611	0.238884	0.326043	0.263942
...	...	...	...	...	...	...	...	...	...	...
ZXDB	-0.002925	-0.080670	-0.024100	0.027726	-0.114968	-0.109545	-0.005998	-0.023018	-0.091592	-0.145058
ZXDC	-0.340634	-0.592188	-0.441215	-0.241991	-0.334266	-0.054244	-0.550736	-0.568386	-0.568292	-0.347858
ZYX	1.163029	0.813722	1.214162	0.733082	1.197966	0.854732	0.506321	1.124261	1.042311	1.013207
ZZEF1	-0.414575	-0.481934	-0.053903	-0.337179	-0.374649	-0.437583	0.199973	-0.259164	-0.171636	-0.375338
ZZZ3	0.325976	0.308467	0.110256	0.245853	0.591393	0.206770	0.106190	0.207667	0.236792	0.064721

	Rank
Method
limma	552.3
fc	575.6
cd	620.9

	Rank
Method
cd	57.0
limma	126.6
fc	182.5

	Rank
Method
cd	330.7
fc	369.2
limma	373.8